code: def test_basic_loading(caplog):
      caplog.set_level(logging.CRITICAL)
      # slow tokenizers
      tokenizer = Tokenizer.load(
          pretrained_model_name_or_path="bert-base-cased",
          do_lower_case=True,
          use_fast=False,
      )
      assert type(tokenizer) == BertTokenizer
      assert tokenizer.basic_tokenizer.do_lower_case == True
  
      tokenizer = Tokenizer.load(
          pretrained_model_name_or_path="xlnet-base-cased",
          do_lower_case=True,
          use_fast=False,
      )
      assert type(tokenizer) == XLNetTokenizer
      assert tokenizer.do_lower_case == True
  
      tokenizer = Tokenizer.load(
          pretrained_model_name_or_path="roberta-base", use_fast=False
      )
      assert type(tokenizer) == RobertaTokenizer
  
      # fast tokenizers
      tokenizer = Tokenizer.load(
          pretrained_model_name_or_path="bert-base-cased",
          do_lower_case=True,
      )
      assert type(tokenizer) == BertTokenizerFast
      assert tokenizer.do_lower_case == True
  
      tokenizer = Tokenizer.load(
          pretrained_model_name_or_path="xlnet-base-cased",
          do_lower_case=True,
      )
      assert type(tokenizer) == XLNetTokenizerFast
      assert tokenizer.do_lower_case == True
  
      tokenizer = Tokenizer.load(
          pretrained_model_name_or_path="roberta-base"
      )
      assert type(tokenizer) == RobertaTokenizerFast
  
file_path: FARM/test/test_tokenization.py
imports: import logging
  import pytest
  import re
  from transformers import BertTokenizer, BertTokenizerFast, RobertaTokenizer, RobertaTokenizerFast, XLNetTokenizer, XLNetTokenizerFast, ElectraTokenizerFast
  from tokenizers.pre_tokenizers import WhitespaceSplit
  from farm.modeling.tokenization import Tokenizer, tokenize_with_metadata, truncate_sequences
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: test_basic_loading(caplog) without return types